Mobility Data Report

Overview

Dataset statistics

Number of records 2703242
Distinct trips 1351621
Number of complete trips (start and and point) 1351621
Number of incomplete trips (single point) 0
Distinct users 360619
Distinct locations (lat & lon combination) 200063

Additional variable

None

Missing values

User ID (uid) 0
Trip ID (tid) 0
Timestamp (datetime) 0
Latitude (lat) 0
Longitude (lng) 0

TODO: don't show exact values as they might be misleading. Maybe, e.g., with 99% chance there are no missing values (?)
There should not be any missing values within the dataset, except potentially within the additional variable. If there are missing values present in the dataset this is an indicator for a faulty dataset.

Temporal properties

Number of trips over time


Distribution

Min. 2018-04-18
25% 2018-04-19
Median 2018-04-19
75% 2018-04-19
Max. 2018-04-20

Number of trips per weekday

Number of trips per hour of day split by weekday and weekend

Place analysis

Visits per tile

TODO: fix bug that all legends appear in first map

Points outside the given tessellation: 18
The following statistics give insights into the distribution of the visits over the tiles (mean, min, max and quartiles) - whether there are tiles that are visited more often than others or if the visits are distributed equally over all tiles.

Distribution

Min. 16.0
25% 2669.0
Median 5377.0
75% 9905.0
Max. 46572.0
A different way of visualizing the distribution of visits over tiles is achieved by the cumuluated sum of all visits: If only a few tiles include most of all visits, the curve has a steep increase in the beginning and a flat part at the end. If the visits are distributed equally over the entire tiles the line is a straight diagonal.

Ranking most frequently visited tiles

1 Kantstraße / Schillerstraße (Id: 110202420): 46572
2 Schönhauser Allee / Bornholmer Straße (Id: 110510620): 28672
3 Behmstraße / Stettiner Straße (Id: 110500720): 27970
4 Hausvogteiplatz (Id: 110110320): 26084
5 Friedrich-Wilhelm-Platz (Id: 110506110): 23276
6 Südstern (Id: 110401620): 23030
7 Osloer Straße / Seestraße (Id: 110500920): 22992
8 Sonnenallee / Hobrechtstraße (Id: 110407510): 22812
9 Kantstraße / Suarezstraße (Id: 110402410): 22742
10 Müllerstraße / Seestraße (Id: 110500910): 22558

Visits per tile and time window

Weekday: absolute count

Weekday: deviation from average

Origin-destination (OD) analysis

OD flows between tiles

# TODO: fix legend appearing in wrong chart

Intra-tile flows

The number and percentage of flows that start and end within the same tile.

218700.0 (16.18 %) of flows start and end within the same cell.

A large number of intra-cell flows either indicate round-trips (e.g., going running starting and ending at the home location) or a tessellation that is to coarse to properly capture flows.

Distribution

Mean 13.83
Min. 1.0
25% 2.0
Median 4.0
75% 10.0
Max. 4425.0
A different way of visualizing the distribution of number per flows is achieved by the cumuluated sum of all flows: If only a few flows include most of all visits, the curve has a steep increase in the beginning and a flat part at the end. If the visits are distributed equally over the entire flows the line is a straight diagonal.

Most frequent OD connections

Ranking most frequent OD connections

1 Kantstraße / Schillerstraße - Kantstraße / Schillerstraße: 4425.0
2 Wilhelmsruher Damm / Senftenberger Ring - Wilhelmsruher Damm / Senftenberger Ring: 3146.0
3 Friedrich-Wilhelm-Platz - Friedrich-Wilhelm-Platz: 2819.0
4 Frankfurter Allee / Petersburger Straße - Frankfurter Allee / Petersburger Straße: 2460.0
5 Friedrichshagener Straße / Bahnhofstraße - Friedrichshagener Straße / Bahnhofstraße: 2365.0
6 Schönhauser Allee / Bornholmer Straße - Schönhauser Allee / Bornholmer Straße: 2307.0
7 Müggelseedamm / Fürstenwalder Damm - Müggelseedamm / Fürstenwalder Damm: 2293.0
8 Sonnenallee / Hobrechtstraße - Sonnenallee / Hobrechtstraße: 2010.0
9 Wendenschloßstraße / Salvador-Allende-Straße - Wendenschloßstraße / Salvador-Allende-Straße: 1968.0
10 Südstern - Südstern: 1951.0

Trip statistics

Travel time of trips (in minutes)

24354 outliers have been excluded.
Outliers are values above 90

Distribution

Min. 4.0
25% 16.0
Median 27.0
75% 43.0
Max. 90.0

Jump length (in meters)

58818 outliers have been excluded.
Outliers are values above 15000

Distribution

Min. 0.0
25% 834.19
Median 3082.89
75% 5977.36
Max. 14999.65

User analysis

number of trajectories per user

Distribution

Min. 2.0
25% 2.0
Median 4.0
75% 5.0
Max. 16.0

Time between two consecutive trajectories of a user

How much time passes between two consecutive trajeoctories? This information gives insights on the temporal density of the dataset. Trajectories might follow each other consecutively, then the time inbetween only is as long as the stay duration at that place. If the trips are only collected sparsely there might be days between single trajectories of a user.
This analysis is based on the assumption that trips of a user follow each other consecutively and do not overlap, i.e., the start time of a following trip cannot start before the previous one has ended. Therefore, we first perform a plausibility check to ensure that no user trips overlap. Otherwise this might be an indication for a faulty dataset.

Plausibility check: overlapping user trips

There are 25815 cases where the start time of the following trajectory precedes the previous end time.
If there are overlapping trips present in the dataset the minimum time between trajectories will be negative.

Distribution

Min. 0 days 00:00:00
25% 0 days 00:17:00
Median 0 days 01:15:00
75% 0 days 03:40:00
Max. 0 days 22:21:00

Radius of gyration

The radius of gyration is the characteristic distance traveled by an individual during a period of time.
4472 outliers have been excluded.
Outliers are values above 15000

Distribution

Min. 0.0
25% 1380.69
Median 2571.81
75% 4414.05
Max. 9999.99

Location entropy

Location entropy (based on Shannon Entropy) captures the diversity of user visits. If most trips to a certain location originate from a single (or few) user the entropy is low. A high entropy suggests that the place is visited by diverse users evenly. A dataset with many cells with high visit counts but low entropy suggests, that single users drive certain mobility patterns that might not be representative for other users.

Number of distinct tiles per user

TODO: clean outliers - or not necessary as data set is already truncated?
How many different tiles does a single user visit?

Distribution

Min. 1.0
25% 2.0
Median 3.0
75% 3.0
Max. 12.0

Uncorrelated Entropy

The temporal-uncorrelated entropy characterizes the heterogeneity of the users visitation patterns (including the historical probability that a location was visited by the user).
TODO: clean outliers - or not necessary for uncorrelated entropy, as it is normalized?

Distribution

Min. 0.0
25% 0.92
Median 0.96
75% 1.0
Max. 1.0

Real Entropy (including sequence of locations)


TODO: clean outliers - or not necessary for real entropy?
None None